Job Summary

We are seeking a Feature Engineer with 3–5 years of experience in Data Engineering, Feature Engineering, or ML Engineering. The ideal candidate will have strong expertise in Python, SQL, distributed data processing, feature pipelines, feature stores, and cloud platforms, with experience building scalable, production-grade solutions for AI/ML use cases.

Required Technical Skills

• 3–5 years of experience in Data Engineering, Feature Engineering, or ML Engineering.

• Strong hands-on experience designing and developing production-grade batch and real-time feature pipelines.

• Advanced proficiency in Python and SQL.

• Experience with distributed processing frameworks such as Apache Spark or Flink.

• Strong knowledge of feature engineering, feature design patterns, transformations, and aggregation strategies.

• Hands-on experience with feature stores such as Feast, Hopsworks, or Amazon SageMaker.

• Experience working with scalable distributed data systems and enterprise AI/ML platforms.

• Understanding of the ML lifecycle, including feature importance and model input optimization.

• Experience implementing data quality, validation, and drift detection.

• Experience with CI/CD, automated testing, monitoring, and observability.

• Hands-on experience with at least one major cloud platform: AWS, Azure, or GCP.

• Strong understanding of performance tuning, scalability, reliability, and cost optimization.

Roles & Responsibilities

• Design and implement scalable, reusable batch and real-time feature pipelines.

• Develop complex feature transformations, aggregations, and data modeling logic.

• Optimize feature pipelines for performance, latency, scalability, and cost efficiency.

• Establish and maintain feature quality, accuracy, freshness, reliability, and SLA standards.

• Collaborate with Data Scientists and ML Engineers to develop features aligned with business and ML use cases.

• Contribute to feature store architecture, standards, and best practices.

• Implement data validation, monitoring, and feature/drift detection mechanisms.

• Support production deployment, monitoring, troubleshooting, and incident resolution.

• Develop CI/CD pipelines and automated tests for reliable feature delivery.

• Perform performance tuning and optimize distributed data processing workloads.

• Mentor other Feature Engineers and promote engineering best practices and coding standards.

• Contribute to continuous improvement of feature engineering platforms and processes.

Preferred Qualifications

• Prior mentoring or technical leadership experience.

• Experience with enterprise-scale AI/ML platforms or feature stores.

• Strong understanding of production ML systems and MLOps practices.

• Excellent problem-solving, communication, and cross-functional collaboration skills.